A Taxonomy for Patient-Ventilator Interactions and a Method to Read Ventilator Waveforms

Detection and quantitative analysis of patient-ventilator interactions in ventilated infants by deep learning networks

BACKGROUND

The study of patient-ventilator interactions (PVI) in mechanically ventilated neonates is limited by the lack of unified PVI definitions and tools to perform large scale analyses.

METHODS

An observational study was conducted in 23 babies randomly selected from 170 neonates who were ventilated with SIPPV-VG, SIMV-VG or PSV-VG mode for at least 12 h. 500 breaths were randomly selected and manually annotated from each recording to train convolutional neural network (CNN) models for PVI classification.

RESULTS

The average asynchrony index (AI) over all recordings was 52.5%. The most frequently occurring PVIs included expiratory work (median: 28.4%, interquartile range: 23.2-40.2%), late cycling (7.6%, 2.8-10.2%), failed triggering (4.6%, 1.2-6.2%) and late triggering (4.4%, 2.8-7.4%). Approximately 25% of breaths with a PVI had two or more PVIs occurring simultaneously. Binary CNN classifiers were developed for PVIs affecting ≥1% of all breaths (n = 7) and they achieved F1 scores of >0.9 on the test set except for early triggering where it was 0.809.

CONCLUSIONS

PVIs occur frequently in neonates undergoing conventional mechanical ventilation with a significant proportion of breaths containing multiple PVIs. We have developed computational models for seven different PVIs to facilitate automated detection and further evaluation of their clinical significance in neonates.

IMPACT

The study of patient-ventilator interactions (PVI) in mechanically ventilated neonates is limited by the lack of unified PVI definitions and tools to perform large scale analyses. By adapting a recent taxonomy of PVI definitions in adults, we have manually annotated neonatal ventilator waveforms to determine prevalence and co-occurrence of neonatal PVIs. We have also developed binary deep learning classifiers for common PVIs to facilitate their automatic detection and quantification.

Reverse triggering during mechanical ventilation: Diagnosis and clinical implications

This review addresses the phenomenon of "reverse triggering", an asynchrony that occurs in deeply sedated patients or patients in transition from deep to light sedation. Reverse triggering has been reported to occur in 30-90% of all ventilated patients. The underlying pathophysiological mechanisms remain unclear, but "entrainment" is proposed as one of them. Detecting this asynchrony is crucial, and methods such as visual inspection, esophageal pressure, diaphragmatic ultrasound and automated methods have been used. Reverse triggering may have effects on lung and diaphragm function, probably mediated by the level of breathing effort and eccentric activation of the diaphragm. The optimal management of reverse triggering has not been established, but may include the adjustment of ventilatory parameters as well as of sedation level, and in extreme cases, neuromuscular block. It is important to understand the significance of this condition and its detection, but also to conduct dedicated research to improve its clinical management and potential effects in critically ill patients.

Asynchronies during invasive mechanical ventilation: narrative review and update

Invasive mechanical ventilation is a frequent therapy in critically ill patients in critical care units. To achieve favorable outcomes, patient and ventilator interaction must be adequate. However, many clinical situations could attempt against this principle and generate a mismatch between these two actors. These asynchronies can lead the patient to worst outcomes; because of that is vital to recognize and treat these entities as soon as possible. Early detection and recognition of the different asynchronies could favor the reduction of the days of mechanical ventilation, the days of hospital stay, and in intensive care and improve clinical results.